Bei der Datenintegration werden Daten aus verschiedenen Quellen zusammengeführt, kombiniert und in einem geeigneten Format gespeichert.
Integrierte Daten helfen Unternehmen, genauere und fundiertere Entscheidungen zu treffen
Die Datenintegration beschleunigt den Zugang zu den Daten und optimiert den Prozess der Datenanalyse. Dies steigert die Effizienz von Unternehmen.
Die Datenintegration hilft Unternehmen, ihre Risikomanagementprozesse zu verbessern, indem sie Daten aus verschiedenen Quellen zusammenführt.
interaktion
Datenübertragung
Worldwide
Customers
Succeeded
Projects
Integrierte Daten unterstützen die strategischen Planungsprozesse von Unternehmen und helfen ihnen, Zukunftsprognosen zu erstellen.
Our consulting services are designed to optimize your current data infrastructure or build a new, efficient integration system from the ground up. We’ll design a flexible, cost-effective solution architecture, recommend the best tools, and establish a secure, compliant data management framework.
Whether you need to set up and customize a data integration platform or develop custom software components like ETL pipelines and APIs, we have you covered. We can also implement a comprehensive data warehousing and analytics system tailored to your unique requirements.
Our support services ensure the long-term stability of your data integration solution, addressing troubleshooting needs and evolving the system as requirements change. We also monitor and streamline data quality management processes to maintain data accuracy and consistency.
This method involves extracting data from source systems, transforming it based on the desired data model, and loading it into a centralized repository, such as a database or data warehouse. Best for: Batch processing and handling structured data, like sales reporting solutions that aggregate CSV and JSON files from various systems every 24 hours.
In this approach, data is extracted from the source systems, loaded into a centralized storage in its raw form, and transformed later. Best for: Real-time processing and handling large datasets, such as synchronizing an ecommerce platform with internal systems to provide real-time product availability and personalized user recommendations.
Data virtualization creates a unified view of multiple databases without moving or duplicating data physically. Best for: Accessing integrated data from distributed sources that can’t be merged physically, like healthcare providers accessing patient records across multiple hospital systems.
Rather than processing entire datasets, data propagation detects changes in the source data and transmits only those updates to target systems for real-time synchronization. Best for: Dynamic datasets requiring low-latency updates and real-time consistency, such as financial transaction synchronization across banking platforms.
Best for: Legacy systems that are difficult to integrate in a cloud environment or organizations with strict data hosting policies (e.g., healthcare, defense). What you pay for: Software licenses (e.g., Informatica, Oracle Data Integrator), hardware, and IT resources for setup and maintenance. Common challenge: Scalability limitations due to the need for additional hardware. How we solve it: We parallelize tasks across servers to increase processing power and use solid-state drives and in-memory caching to reduce latency.
Best for: Handling growing data volumes, enabling easy access for distributed teams, and leveraging cloud platforms for data analytics, machine learning, and AI. What you pay for: Subscription fees (e.g., AWS Glue) and cloud resource consumption based on usage, along with IT resources for maintenance. Common challenge: Vendor lock-in. How we solve it: By using common data formats (e.g., JSON, XML) for cross-cloud portability and incorporating open-source tools to avoid dependency on specific cloud vendors.
Best for: Companies looking to gradually move from on-premises to cloud infrastructure or those needing to retain some data on-premises due to security or regulatory concerns. What you pay for: Costs associated with both on-premises and cloud solutions, along with IT support for implementation and maintenance. Common challenge: Data consistency between cloud and on-premises systems. How we solve it: We implement data replication and synchronization strategies to maintain consistent data updates across both environments.
